
> # set up environment ----
> library(data.table)

> library(ggplot2)

> library(cowplot)

> library(parallel)

> source("R/00-functions.R")

> load("data/analysis_data.RData")

> RNGkind("L'Ecuyer-CMRG")

> set.seed(356024460)

> # regressions ----
> #   heterogeneity by gender ----
> fmla_list <- list(ranking ~ electability_last * male_candidate)

> #     main dataset ----
> data_main <- respondent_candidate_data[main == 1]

> id_main <- data_main[, unique(subject_id)]

> n_main <- length(id_main)

> data_list_main <- lapply(id_main, function(i) {
+   data_main[subject_id == i]
+ })

> models_main <- lapply(fmla_list, lm, data = data_main)

> boot <- function(i) {
+   boot_ids <- sample(n_main, n_main, replace = TRUE)
+   boot_data <- rbindlist(lapply(boot_ids, function(i) {
+     data_list_main[[i]]
+   }))
+   boot_models <- lapply(fmla_list, lm, data = boot_data)
+   lapply(boot_models, coef)
+ }

> boots_main <- mclapply(1:1000, boot, mc.cores = 6)

> boot_coefs_main <- rbindlist(lapply(boots_main, function(y) as.data.table(rbind(y[[1]]))))

> #     full dataset ----
> data_full <- copy(respondent_candidate_data)

> id_full <- data_full[, unique(subject_id)]

> n_full <- length(id_full)

> data_list_full <- lapply(id_full, function(i) {
+   data_full[subject_id == i]
+ })

> models_full <- lapply(fmla_list, lm, data = data_full)

> boot <- function(i) {
+   boot_ids <- sample(n_full, n_full, replace = TRUE)
+   boot_data <- rbindlist(lapply(boot_ids, function(i) {
+     data_list_full[[i]]
+   }))
+   boot_models <- lapply(fmla_list, lm, data = boot_data)
+   lapply(boot_models, coef)
+ }

> boots_full <- mclapply(1:1000, boot, mc.cores = 6)

> boot_coefs_full <- rbindlist(lapply(boots_full, function(y) as.data.table(rbind(y[[1]]))))

> # plots ----
> electability_effects_main <- rbind(
+   boot_coefs_main[, .(
+     model = 1,
+     type = "Men",
+     est = mean(electability_last + `electability_last:male_candidate`),
+     q025 = quantile(
+       electability_last + `electability_last:male_candidate`, .025),
+     q975 = quantile(
+       electability_last + `electability_last:male_candidate`, .975))],
+   boot_coefs_main[, .(
+     model = 1,
+     type = "Women",
+     est = mean(electability_last),
+     q025 = quantile(electability_last, .025),
+     q975 = quantile(electability_last, .975))])

> electability_effects_full <- rbind(
+   boot_coefs_full[, .(
+     model = 1,
+     type = "Men",
+     est = mean(electability_last + `electability_last:male_candidate`),
+     q025 = quantile(
+       electability_last + `electability_last:male_candidate`, .025),
+     q975 = quantile(
+       electability_last + `electability_last:male_candidate`, .975))],
+   boot_coefs_full[, .(
+     model = 1,
+     type = "Women",
+     est = mean(electability_last),
+     q025 = quantile(electability_last, .025),
+     q975 = quantile(electability_last, .975))])

> electability_effects_main[, type := factor(type,
+   levels = c("Men", "Women", "White/Man", "Straight/\nWhite/Man", "Other Candidates"))]

> electability_effects_full[, type := factor(type,
+   levels = c("Men", "Women", "White/Man", "Straight/\nWhite/Man", "Other Candidates"))]

> # plots ----
> theme_set(theme_bw() +
+     theme(
+       legend.position = "none",
+       strip.text.x = element_blank(),
+       strip.background = element_blank()))

> x1 <- dcast(electability_effects_main[model == 1, .(type, est)],
+   . ~ type, value.var = "est")

> x1[, model := 1]

> g2 <- ggplot(electability_effects_main[model == 1], aes(type, est)) +
+   facet_grid(~ model, scales = "free_x") +
+   geom_hline(yintercept = 0, linetype = 3) +
+   geom_point() +
+   geom_errorbar(aes(ymin = q025, ymax = q975), width = 0) +
+   geom_segment(data = x1, aes(
+     xend = "Women",
+     yend = electability_effects_main$est[2],
+     x = "Men",
+     y = electability_effects_main$est[1])) +
+   xlab("") + ylab("") +
+   theme(strip.background = element_blank()) +
+   scale_y_reverse()

> x1 <- .03

> y1 <- 0

> y2 <- .12

> n <- 1

> h <- (1 - y2) / 1

> cairo_pdf("figures/candidates-main-nonreversed.pdf", 4, 4)

> cowplot::ggdraw() +
+   draw_plot(g2, x1, y1 + 0 * h, 1 - x1, h) +
+   draw_label("Salience Effect", x1/2, .5,
+     angle = 90, size = 10) +
+   draw_label("Electability Salience Decreases\nRankings of Women Candidates",
+     x1 + (1 - x1) / 2, 1 - y2 * .4, size = 13)

> dev.off()
null device 
          1 

> x1 <- dcast(electability_effects_full[model == 1, .(type, est)],
+   . ~ type, value.var = "est")

> x1[, model := 1]

> g2 <- ggplot(electability_effects_full[model == 1], aes(type, est)) +
+   facet_grid(~ model, scales = "free_x") +
+   geom_hline(yintercept = 0, linetype = 3) +
+   geom_point() +
+   geom_errorbar(aes(ymin = q025, ymax = q975), width = 0) +
+   geom_segment(data = x1, aes(
+     xend = "Women",
+     yend = electability_effects_full$est[2],
+     x = "Men",
+     y = electability_effects_full$est[1])) +
+   xlab("") + ylab("") +
+   theme(strip.background = element_blank()) +
+   scale_y_reverse()

> x1 <- .03

> y1 <- 0

> y2 <- .12

> n <- 1

> h <- (1 - y2) / 1

> cairo_pdf("figures/candidates-full-nonreversed.pdf", 4, 4)

> cowplot::ggdraw() +
+   draw_plot(g2, x1, y1 + 0 * h, 1 - x1, h) +
+   draw_label("Salience Effect", x1/2, .5,
+     angle = 90, size = 10) +
+   draw_label("Benefits of Electability (All Respondents)",
+     x1 + (1 - x1) / 2, 1 - y2 * .4, size = 13)

> dev.off()
null device 
          1 

> save(boot_coefs_main, file = "results/boots_coefs_main-nonreversed.RData")
